Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK
Dean Wampler

Dean Wampler
VP, Fast Data Engineering, Lightbend

Website | @deanwampler

Dean Wampler is the vice president of fast data engineering at Lightbend, where he leads the creation of the Lightbend Fast Data Platform, a distribution of scalable, distributed stream processing tools including Spark, Flink, Kafka, and Akka, with machine learning and management tools. Dean is the author of Programming Scala and Functional Programming for Java Developers and the coauthor of Programming Hive, all from O’Reilly. He is a contributor to several open source projects. A frequent Strata speaker, he’s also the co-organizer of several conferences around the world and several user groups in Chicago.

Sessions

13:3017:00 Tuesday, 22 May 2018
Dean Wampler (Lightbend), Boris Lublinsky (Lightbend)
Average rating: ***..
(3.67, 3 ratings)
Dean Wampler and Boris Lublinsky walk you through building streaming apps as microservices using Akka Streams and Kafka Streams. Along the way, Dean and Boris discuss the strengths and weaknesses of each tool for particular design needs and contrast them with Spark Streaming and Flink, so you'll know when to chose them instead. Read more.
14:5515:35 Wednesday, 23 May 2018
Dean Wampler (Lightbend)
Streaming data systems, so called fast data, promise accelerated access to information, leading to new innovations and competitive advantages. But they aren't just faster versions of big data. They force architecture changes to meet new demands for reliability and dynamic scalability, more like microservices. Dean Wampler outlines what you need to know to exploit fast data successfully. Read more.
14:0514:45 Thursday, 24 May 2018
Ask Me Anything
Location: Capital Suite 14
Dean Wampler (Lightbend), Boris Lublinsky (Lightbend)
Join Dean Wampler and Boris Lublinsky to discuss all things streaming: architecture, implementation, streaming engines and frameworks, techniques for serving machine learning models in production, traditional big data systems (dying or still relevant?), and general software architecture and data systems. Read more.